At least for applications within the fields of computer science and information science, it is sometimes useful to utilize a listing of words and/or phrases that are grouped based on category. For example, a listing might be indicative of the fact that “Academy Award,” “Turing award,” and “Nobel prize” are all kinds of awards. Information in such a listing can be utilized for a wide variety of different purposes such as, but certainly not limited to, serving as a basis for improving Internet search processes and/or displaying search results more appropriately. Unfortunately, keeping a listing updated through consistent expansion to include new accurately grouped words and/or phrases is not typically a straightforward or efficient undertaking.
One option is to derive words and/or phrases from a large collection of regular text such as magazine articles, newspaper articles or any text available on the web. In such a case, it is possible to do some level of categorization based on characteristics of contextual words that occur on either or both sides of a candidate word or words. Unfortunately; however, within regular text, contextual words are somewhat likely to be limited in terms of their ability to serve as an effective basis for categorization. Determining which contextual words are appropriate to support categorization is a complicated undertaking.
The discussion above is merely provided for general background information and is not intended for use as an aid in determining the scope of the claimed subject matter.